Field of the Invention
The disclosure as detailed herein is in the technical field of data analytics. More specifically, the present disclosure relates to the technical field of predictive modeling.
Description of Related Art
There exists a consistent need for reliable predictive data across a variety of applications, such as but not limited to the following: maintaining national security; forecasting financial markets, political outcomes and retail trends; and furthering scientific advancements through the processing of mass amounts of data. There currently exist several tools and techniques that permit the automated analysis of data to determine or predict certain future outcomes. These typically use statistical techniques and machine learning algorithms to create predictive models based on collected data. As close as we would like to say that these techniques come to mimicking the human brain; there is no hardware or software that can effectively emulate the subtle insights and intuition that a human Subject Matter Expert (SME) possesses.
It is thought that a preferred embodiment of the invention may improve the well-being of multiple types of people and entities, including but not limited to: military analysts, financial analysts, data scientists, molecular biologists, retail analysts, risk analysts, political analysts, and forecasting organizations. For example, instances of threats to national security are a major concern for governments, politicians, and military analysts. Determining the likelihood and viability of such a threat requires information, expediency, and resources, as well as a level of insight and accuracy that many singular entities do not possess.
In the field of finance, there are numerous instances of analysts making bad forecasts which result in a financial loss for individuals, schools, banks, and other clients. Therefore, it is important that analysts are able to accurately assess information to predict market trends in order to assess the future performance of stocks and investments, and to provide quality guidance to their clients. The addition of inputs from subject matter experts to machine learning will improve the effectiveness of prediction.
For all manner of scientists, data are essential to research and study. It is well known that there is a reliance on machine learning for model creation. Traditional machine learning relies heavily on “training data”, which are too much for humans alone to create a workable model and to analyze. In this regard, having a better way for scientists to synthesize and analyze data would result in increased productivity in research and scientific advancement.